CN114491997A - Virtual power plant operation optimization method and system considering demand response and electric automobile - Google Patents
Virtual power plant operation optimization method and system considering demand response and electric automobile Download PDFInfo
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Abstract
A virtual power plant operation optimization method and system considering demand response and electric automobiles comprises the following steps: step 1, constructing a demand response strategy of a virtual power plant based on time-of-use electricity prices and interruptible loads, and transferring partial loads from a peak to a valley; step 2, on the basis of the step 1, a mixed integer linear programming model of the virtual power plant is constructed by combining the generated energy of the DG, the charging and discharging of the electric automobile and the demand response; and 3, solving the mixed integer linear programming model of the virtual power plant obtained in the step 2 to obtain the operating conditions of the virtual power plant, issuing the solving result to the controller, and executing the solving result on the day of the operation of the virtual power plant. The invention can effectively promote and improve the utilization efficiency of energy, reduce the operation cost of a virtual power plant, improve the profit and provide technical support for the utilization of renewable energy.
Description
Technical Field
The invention belongs to the technical field of virtual power plants, and particularly relates to a virtual power plant operation optimization method and system considering demand response and electric automobiles.
Background
With the increasing demand of people for more reliable energy systems, the diversity of energy use is also increasing, the consumption of energy resources is reduced, and the energy utilization efficiency is improved. One approach to overcoming the above challenges is to integrate multiple generator sets (DG) in the form of Virtual Power Plants (VPP). A VPP is a combination of a DG, a schedulable load, and distributed energy storage to support and control various types of DGs through communication technologies. The generation uncertainty of DG, skew loss and increase of total profit can be reduced by VPP.
In the prior art, researchers have proposed a risk-based stochastic framework for considering Demand Response (DR) participating VPP short-term energy and reserve scheduling. The proposed scheduling framework is a stochastic programming of risk constraints to maximize the profit of the VPP, taking into account load, wind energy and electricity price uncertainties.
In the prior art, more research focuses on the mere participation of DR in the scheduling of VPP. With the increasing popularity of Electric vehicles (Electric vehicles), grid-connected Electric vehicles have been proposed as an Energy Storage System (DESS) supply solution that is gaining attention. Scholars have proposed a demand response model for electric vehicle charging and discharging management to reduce the energy supply cost of electric power retailers. Most studies in this regard use a cost function with the goal of minimizing the energy cost of charging an electric vehicle, and few previous studies have considered both DR and electric vehicles in the objective function.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide a virtual power plant operation optimization method and system considering demand response and electric vehicles, promote and improve energy utilization efficiency, reduce the operation cost of a virtual power plant, improve profits and provide technical support for the utilization of renewable energy.
The invention adopts the following technical scheme. The invention relates to a virtual power plant operation optimization method considering demand response and electric automobiles, which comprises the following steps: step 1, constructing a demand response strategy of a virtual power plant based on time-of-use electricity prices and interruptible loads, and transferring partial loads from a peak to a valley; step 2, on the basis of the step 1, a mixed integer linear programming model of the virtual power plant is constructed by combining the generated energy of the DG, the charging and discharging of the electric automobile and the demand response; and 3, solving the mixed integer linear programming model of the virtual power plant obtained in the step 2 to obtain the operating conditions of the virtual power plant, issuing the solving result to the controller, and executing the solving result on the day of the operation of the virtual power plant.
Preferably, step 1 specifically comprises: step 1.1, considering demand response based on time-of-use electricity price and demand response of a virtual power plant constructed based on excitation interruption load, transferring partial load from a peak to a valley, and realizing load balancing; step 1.2, constructing a demand response constraint condition of a virtual power plant, wherein the total power consumption of a load is kept unchanged during optimization; and 1.3, constructing a load interruption cost function.
Preferably, in step 1.1, the load balancing is expressed by the following equation (1),
D(L，t)＝d(L，t)+dup(L，t)-ddo(L，t) (1)
in the formula:
d (L, t) represents the power consumption of the load L at the time t after the time-of-use electricity price is executed,
d (L, t) represents the predicted power consumption of the load L at time t,
dup(L, t) represents the power consumption of the load L increased in response to the original price change at time t,
ddo(L, t) represents the power consumption of the load L reduced in response to the change in the original price at time t.
Preferably, in step 1.2, the total power consumption of the load should remain unchanged, as expressed in the following equation (2),
in the formula:
t denotes the optimization duration.
Preferably, in step 1.3, the load interruption cost function is expressed by the following equation (7),
CIL(j，t)＝β(j)QIL(j，t) (7)
in the formula:
CIL(j, t) represents the cost of outage reduction,
beta (j) represents the cost of an interrupt or the cost of a delay load j,
QIL(j, t) represents the amount of power that the interruptible load decreases at time t.
Preferably, step 2 specifically comprises: step 2.1, constructing a virtual power plant single-day scheduling cost function, comprising the following steps: the cost part of DG power generation of the virtual power plant, the cost part of receiving power from a main power grid, the cost part of electric vehicle charging and discharging, the cost part of load interruption and the cost part of electric vehicle response; and 2.2, constructing a constraint condition of a virtual power plant single-day scheduling cost objective function, wherein the virtual power plant single-day scheduling cost objective function is limited and constrained by an actually existing power grid and demand response.
Preferably, in step 2.1, the one-day scheduling cost function of the virtual power plant is expressed by the following formula (8),
f＝f1+f2+f3+f4+f5 (8)
in the formula:
f represents a virtual power plant single-day scheduling cost function,
f1represents the cost of the electricity generated by the DG,
f2representing the cost of receiving power from the main grid,
f3represents the cost of charging and discharging the electric vehicle,
f4which represents the cost of interrupting the load and,
f5representing the cost of electric vehicle demand response.
Preferably, step 2.1 specifically comprises: step 2.1.1, constructing a cost function of DG power generation of the virtual power plant, which is expressed by the following formula (9),
in the formula:
f1which represents the cost of the electricity generation,
PDc(DG, t) represents the power generation of the DG unit at time t,
CDG(DG, t) represents the cost of electricity generation of the DG unit at time t,
NDGrepresenting the number of available DG unit resources;
step 2.1.2, a cost function for receiving power from the main grid is constructed, expressed by the following equation (10),
in the formula:
f2representing the cost of receiving power from the main grid,
PG(t) represents the amount of power received from the main grid at time t,
CG(t) represents the electricity price of the amount of electricity purchased from the grid at time t,
when VPP injects power into the main grid at time t, FGThe amount of (t) is negative, when f2Is a negative value; step 2.1.3, constructing a cost function of electric vehicle charging and discharging, which is expressed by the following formula (11),
in the formula:
PDisCharge(V, t) represents the discharge power of the vehicle V at time t,
CDisCharge(V, t) represents the discharge price of the vehicle V at time t,
PCharge(V, t) represents the charging power of the vehicle V at time t,
CCharge(V, t) represents the charge price of the vehicle V at time t,
NVrepresenting the total number of electric vehicles;
step 2.1.4, a cost function of the interrupt load is constructed, expressed by the following equation (12),
in the formula:
f4the cost of the interrupt load is represented,
PIL(L, t) represents the amount of power used by the load L at the time t,
CIL(L, t) represents the price of the power supply at time t,
NILrepresenting the number of interruptible loads;
step 2.1.5, constructing a cost function of the response of the electric automobile, which is expressed by the following formula (13),
in the formula:
ETripRed(V, t) represents the electric power reduced by the reduction of the running distance of the vehicle V at the time t,
CTripRed(V, t) represents E of the vehicle VTripRed(V, t) price at time t,
z (V) represents a travel time change of the vehicle V, is a binary variable,
CShift(V) represents the price of the travel time change of the vehicle V.
Preferably, step 2.2 specifically comprises: step 2.2.1, constraints of the generated energy and the power consumption are constructed, which are expressed by the following formula (15),
step 2.2.2, constructing the limit of the generation amount of each DG of the virtual power plant, which is expressed by the following formula (16),
in the formula:
PDGmin(DG, t) represents the minimum value of the DG power generation amount at time t,
PDGmax(DG, t) represents the maximum value of DG power generation at time t;
step 2.2.3, constructing a virtual power plant to accept the electric energy limit of the main power grid, which is expressed by the following formula (17),
in the formula:
PGmin(t) represents the minimum value of electrical energy received from the main grid at time t,
PCmax(t) indicates the reception of electricity from the main grid at time tMaximum value of energy;
step 2.2.4, constructing an electric vehicle charging mode constraint, and defining two binary decision variables X (V, t) and Y (V, t); x (V, t) is related to the charge mode, Y (V, t) is related to the discharge mode, and is expressed by the following equation (18),
in the formula:
x (V, t) represents a charge mode decision variable, 1 only when the vehicle is in charge mode,
y (V, t) represents a discharge mode decision variable, 1 only when the vehicle is in discharge mode;
step 2.2.5, constructing an electric vehicle battery energy storage constraint, represented by the following formula (19),
in the formula:
EStore(V, t) represents the stored energy of the vehicle V battery at the current time interval,
EStore(V, t-1) represents the charge level of the battery at a time interval preceding the vehicle V,
PCharge(V, t) represents the amount of charge of the previous time interval,
ETrip(V, t) represents the current interval trip electric energy consumption,
PDisCharge(V, t) represents the amount of discharge of the battery at the current interval;
step 2.2.6, constructing a battery charging amount limit of the electric automobile, which is expressed by the following formula (20),
in the formula:
EBatCap(V) the battery capacity of the vehicle VThe amount of the compound (A) is,
ηc(V) represents the charging efficiency of the vehicle V;
step 2.2.7, constructing the discharge capacity limit of the battery of the electric automobile, which is expressed by the following formula (21),
in the formula:
ηd(V) represents the discharge efficiency of the vehicle V;
step 2.2.8, constructing the energy storage limit of the battery of the electric automobile, which is expressed by the following formula (22),
at step 2.2.9, constructing an electric vehicle trip electric energy demand limit, represented by the following equation (23),
in the formula:
EMinCharge(V, tLast) represents the minimum storage amount of battery power before the last trip.
Preferably, in step 3, before issuing the solution result to the controller for execution, the method further includes: the feasibility and the correctness of the virtual power plant operation optimization method considering the demand response and the electric automobile are verified through simulation.
Preferably, the simulation verification comprises the steps of carrying out simulation on an electric vehicle unordered charging scene which does not participate in demand response and an electric vehicle intelligent charging scene based on price demand response, and comparing simulation results.
The invention relates to a virtual power plant operation optimization system considering demand response and electric vehicles, and the virtual power plant operation optimization method considering demand response and electric vehicles is operated according to the first aspect of the invention.
Compared with the prior art, the invention has the beneficial effects that at least: firstly, a demand response mechanism of interruptible load and time-of-use electricity price is considered, then a Mixed Integer Linear Programming (MILP) model of a virtual power plant based on demand response and an electric vehicle is constructed, constraint problems of a generator, the electric vehicle and the like are considered, finally feasibility and effectiveness of a simulation experiment verification method are utilized, the problem of energy distribution in VPP can be effectively solved, and operation cost is saved.
Drawings
FIG. 1 is a diagram of a virtual power plant structure in a simulation model of a virtual power plant operation optimization method of the present invention considering demand response and electric vehicles;
FIG. 2 is a virtual power plant load curve in the virtual power plant operation optimization method considering demand response and electric vehicles according to the present invention;
FIG. 3 is a distribution diagram of the number of electric vehicles connected to each bus in a virtual power plant operation optimization method considering demand response and electric vehicles according to the present invention;
fig. 4 is a schematic diagram of the number of grid-connected electric vehicles and the number of travel vehicles 24 hours a day in the virtual power plant operation optimization method considering demand response and electric vehicles according to the present invention;
FIG. 5 is a schematic diagram of a grid load when an electric vehicle does not participate in a demand response in the virtual power plant operation optimization method considering the demand response and the electric vehicle according to the present invention;
FIG. 6 is a schematic diagram of a grid load when an electric vehicle participates in a demand response in the virtual power plant operation optimization method considering demand response and the electric vehicle according to the present invention;
FIG. 7 is a schematic diagram of power consumption within 24 hours before and after a load response in a virtual power plant operation optimization method considering demand response and electric vehicles according to the present invention;
FIG. 8 is a flow chart of a virtual power plant operation optimization method of the present invention that considers demand response and electric vehicles.
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.
In a preferred but non-limiting embodiment of the invention,
it is further preferred that the first and second liquid crystal compositions,
as shown in fig. 8, embodiment 1 of the present invention provides a virtual power plant operation optimization method considering demand response and electric vehicles, including the following steps:
step 1.1, simultaneously considering the demand response based on the time-of-use electricity price and the demand response strategy of the virtual power plant constructed based on the excitation interruption load, filling the valley (low load hours) in order to achieve the aim of reducing the load peak value, balancing the load, transferring partial load from the peak to the valley, and balancing the load as shown in the following formula (1),
D(L，t)＝d(L，t)+dup(L，t)-ddo(L，t) (1)
in the formula:
d (L, t) represents the power consumption of the load L at the time t after the time-of-use electricity price is executed,
d (L, t) represents the predicted power consumption of the load L at time t,
dup(L, t) represents the power consumption of the load L increased in response to the original price change at time t,
ddo(L, t) represents the power consumption of the load L reduced in response to the change in the original price at time t.
Step 1.2, building a demand response constraint condition of the virtual power plant, more specifically, the power consumption of the load can be increased or decreased every hour, but during the optimization, the total power consumption of the load should be kept unchanged, as expressed by the following formula (2),
in the formula:
t represents the optimization duration, is 1-24 h and is one day.
Similarly, the load is set with a specific range and only a portion of the load participates in price-based demand response. Therefore, the upper and lower boundaries of the variable, i.e., the load L, respond to the increased power consumption d of the original price change at time tup(L, t) and reduced power consumption ddo(L, t) must be limited as expressed by the following formulas (3) to (6),
Bup(L)≥dup(L，t)≥0 (3)
Bdo(L)≥ddo(L，t)≥0 (4)
in the formula:
Bup(L) represents the maximum allowable increase of the load L, Bdo(L) represents the maximum allowable reduction of the load L, Bup(L) and Bdo(L) determining an upper limit of load displacement,
εup(L) represents the modulus of elasticity, ε, of an increase in the load Ldo(L) represents the decreasing elastic coefficient of the load L, determines the lower limit of the load displacement,
pr (t) denotes the price at time t, PrrefRepresenting the reference price at time t.
Step 1.3, a load interruption cost function is constructed, and more specifically, load interruption can reduce demand during peak hours or during periods of power system failure. Load break can therefore be considered as a special reserve capability, which can increase the flexibility of the demand side, reduce the flexible standby cost and the optimal power allocation of the demand side, the load break cost function is expressed as the following equation (7),
CIL(j，t)＝β(j)QIL(j，t) (7)
in the formula:
CIL(j, t) represents the cost of outage reduction,
β (j) represents the cost of the interrupt or the cost of the delay load j, the value of β (j) is determined according to the contract between the VPP and the demand side,
QIL(j, t) represents the amount of power that the interruptible load decreases at time t.
And 2, considering the generated energy of the DG, the charging and discharging of the electric automobile and demand response, and constructing a mixed integer linear programming model of the virtual power plant. Specifically, the problem under consideration is expressed as a mixed integer linear program with the goal of managing energy for the next day based on scheduling the generation of electricity by DG, the charging and discharging of electric cars, and the demand response that makes virtual power plants possible. Electrical energy generating equipment in a virtual power plant includes electric car batteries, various types of Distributable Unit (DU) type DG (such as, but not limited to, microturbines) and Non-distributable Unit (NDU) type DG (such as, but not limited to, photovoltaic and/or wind turbines), and responsive loads. The virtual power plant is also able to exchange energy with the main grid. Furthermore, the operator may sell excess electricity to the main grid, thereby maximizing the profit of the virtual power plant. When discharging and charging a V2G (Vehicle to Grid) electric Vehicle, it is necessary to take into account the living necessities and consumption patterns of the electric Vehicle owner.
In a preferred but non-limiting embodiment of the invention, step 2 comprises in particular:
step 2.1, the energy devices in the VPP include electric vehicle batteries, various types of Distributable Units (DU) and non-distributable units (NDU) and corresponding loads, and a scheduling cost function for a single day is constructed in consideration of demand response, including: the cost part of DG power generation of the virtual power plant, the cost part of receiving power from a main power grid, the cost part of electric vehicle charging and discharging, the cost part of load interruption and the cost part of electric vehicle response; more specifically, the virtual plant single-day scheduling cost function is expressed by the following formula (8),
f＝f1+f2+f3+f4+f5 (8)
in the formula:
f represents a virtual power plant single-day scheduling cost function,
f1represents the cost of the electricity generated by the DG,
f2representing the cost of receiving power from the main grid,
f3represents the cost of charging and discharging the electric vehicle,
f4which represents the cost of interrupting the load and,
f5representing the cost of the electric vehicle demand response.
It should be noted that the scheduling cost function of the virtual power plant for a single day given in the present invention is only a preferred but non-limiting implementation, and includes the following construction of each cost function, which is given in the present embodiment as a preferred way, and it falls within the scope of the present invention to use more specific cost functions, or less cost functions, or other types of cost functions, and to use other ways to calculate the cost functions.
In a preferred but non-limiting embodiment, the single day may be the next day, or any other day in the future.
Further preferably, step 2.1 specifically comprises:
step 2.1.1, constructing a cost function of DG power generation of the virtual power plant, which is expressed by the following formula (9),
in the formula:
f1which represents the cost of the electricity generation,
PDG(DG, t) represents the power generation of the DG unit at time t,
CDG(DG, t) represents the cost of electricity generation of the DG unit at time t,
NDGrepresenting the amount of available DG crew resources.
Step 2.1.2, a cost function for receiving power from the main grid is constructed, expressed by the following equation (10),
in the formula:
f2representing the cost of receiving power from the main grid,
PG(t) represents the amount of power received from the main grid at time t,
CG(t) represents the amount of electricity purchased from the grid at time t,
notably, when VPP injects power into the main grid at time t, PGThe amount of (t) is negative, when f2Is negative.
Step 2.1.3, constructing a cost function of electric vehicle charging and discharging, which is expressed by the following formula (11),
in the formula:
PDisCharge(V, t) represents the discharge power of the vehicle V at time t,
CDisCharge(V, t) represents the discharge price of the vehicle V at time t,
PCharge(V, t) represents the charging power of the vehicle V at time t,
CCharge(V, t) represents the charge price of the vehicle V at time t,
NVrepresenting the total number of electric vehicles.
Step 2.1.4, a cost function of the interrupt load is constructed, expressed by the following equation (12),
in the formula:
f4the cost of the interrupt load is represented,
PIL(L，t)representing the amount of power used by the load L at the interruption of time t,
CIL(L, t) represents the price of the power supply at time t,
NILrepresenting the number of interruptible loads.
Step 2.1.5, constructing a cost function of the response of the electric automobile, which is expressed by the following formula (13),
in the formula:
ETripRed(V, t) represents the electric power reduced by the reduction of the running distance of the vehicle V at the time t,
CTripRed(V, t) represents E of the vehicle VTripRed(V, t) price at time t,
z (V) represents a travel time change of the vehicle V, is a binary variable,
CShift(V) represents the price of the travel time change of the vehicle V.
It is noted that the demand response of the electric vehicle is considered as a reduction in travel distance and a change in travel time. For this purpose, the owners of electric vehicles are contracted in advance, and if they reduce the distance of the trip and change the time of the trip, a reward is given. Therefore, the demand response using this method incurs costs, which are an incentive for the owner of the electric vehicle. Thus, the last term of the virtual plant cost objective function is related to the response cost of the electric vehicle.
Considering the demand response in conjunction with steps 2.1.1 to 2.1.5, the scheduling cost function for a single day (next day) is represented by equation (14) below, with the goal of optimizing the operating conditions of the virtual power plant by managing the energy for the next day, requiring the cost function in equation (14) to be minimized;
it is noted that this function is defined based on time, as shown by the different terms of the virtual plant's scheduling cost function for a single day. The scheduling interval is arbitrary and can be divided into 24 segments each day, and each time interval is 1 hour. The time interval may also be defined as 30 minutes or 15 minutes, but it should be noted that an increase in the number of time intervals will increase the time and computational effort for optimal scheduling.
And 2.2, constructing a constraint condition of a virtual power plant single-day scheduling cost objective function, wherein the virtual power plant single-day scheduling cost objective function must consider all limits and constraints of the actually existing power grid and demand response. In a preferred but non-limiting embodiment of the invention, step 2.2 comprises in particular:
step 2.2.1, constructing the constraints of the generated energy and the power consumption, the sum of the generated power of the virtual power plant DGs, the discharging capacity of the vehicle and the power supply and the purchasing power equal to the consumption capacity of the load from the power grid, which is expressed by the following formula (15),
step 2.2.2, constructing the limit of the generation amount of each DG of the virtual power plant, which is expressed by the following formula (16),
in the formula:
PDGmin(DG, t) represents the minimum value of the DG power generation amount at time t,
PDGmax(DG, t) represents the maximum value of the DG power generation amount at time t.
Step 2.2.3, constructing a virtual power plant to receive the electric energy limit of the main power grid, which is expressed by the following formula (17),
in the formula:
PCmin(t) represents the minimum value of electrical energy received from the main grid at time t,
FGmax(t) represents the maximum amount of power received from the main grid at time t.
Step 2.2.4, constructing an electric vehicle charging mode constraint, and defining two binary decision variables X (V, t) and Y (V, t); x (V, t) is related to the charge mode, Y (V, t) is related to the discharge mode, and is expressed by the following equation (18),
in the formula:
x (V, t) represents a charge mode decision variable, 1 only when the vehicle is in charge mode,
y (V, t) represents a discharge mode decision variable, which is 1 only when the vehicle is in discharge mode.
Step 2.2.5, constructing an electric vehicle battery energy storage constraint, wherein the energy storage of the electric vehicle battery in the current time interval is determined by the electric quantity of the battery in the previous time interval, the charging quantity of the battery in the previous time interval, the electric energy consumption of the trip in the current interval and the discharging quantity of the battery in the current time interval, and is expressed by the following formula (19),
in the formula:
EStore(V, t) represents the stored energy of the vehicle V battery at the current time interval,
EStore(V, t-1) represents the charge level of the battery at a time interval preceding the vehicle V,
PCharqe(V, t) represents the amount of charge of the previous time interval,
ETrip(V, t) represents the current interval trip electric energy consumption,
PDisCharqe(V, t) represents the amount of discharge of the battery at the present interval.
Step 2.2.6, constructing a battery charging amount limit of the electric automobile, which is expressed by the following formula (20),
in the formula:
EBatCap(V) represents the battery capacity of the vehicle V.
ηc(V) represents the charging efficiency of the vehicle V.
Step 2.2.7, constructing the discharge capacity limit of the battery of the electric automobile, which is expressed by the following formula (21),
in the formula:
ηd(V) represents the discharge efficiency of the vehicle V.
Step 2.2.8, constructing the energy storage limit of the battery of the electric automobile, which is expressed by the following formula (22),
at step 2.2.9, constructing an electric vehicle trip electric energy demand limit, represented by the following equation (23),
in the formula:
EMinCharge(V, tLast) represents the minimum storage amount of battery power before the last trip.
It should be noted that the sequence of the above steps 2.2.1 to 2.2.9 can be adjusted arbitrarily, and the arbitrary sequence for completing the above construction of the constraint condition falls within the scope of the core concept of the present invention.
And 3, solving the mixed integer linear programming model of the virtual power plant obtained in the step 2 to obtain the operating conditions of the virtual power plant, issuing the solving result to the controller, and executing the solving result on the day of the operation of the virtual power plant.
In a further preferred but non-limiting embodiment of the present invention, before issuing the solution result to the controller for execution, the method further includes: the feasibility and the correctness of the virtual power plant operation optimization method considering the demand response and the electric automobile are verified through simulation. More specifically, the simulation verification comprises the steps of carrying out simulation on an electric vehicle unordered charging scene which does not participate in demand response and an electric vehicle intelligent charging scene based on price demand response, and comparing simulation results.
In order to more clearly describe the embodiment of the present invention, the following description is made of an operation example based on the present invention, as shown in fig. 1 to 8.
(1) Case description
In this case, the VPP used is a 32 bus 12.66kW system. The structure of the VPP is shown in FIG. 1. It consists of 218 users with a peak consumption value of 4.2 GW. From fig. 1, there are 66 DG in VPP, and the DG types and characteristics in VPP are shown in table 1.
TABLE 1DG types and characteristics
| DG type | Number of units | Minimum power (kW) | Maximum power (kW) | Total capacity (kW) |
| |
32 | 3 | 30 | 558 |
| |
5 | 100 | 200 | 700 |
| |
1 | 10 | 10 | 10 |
| |
15 | 10 | 100 | 10 |
| |
8 | 10 | 50 | 235 |
| |
3 | 100 | 150 | 350 |
| |
2 | 30 | 40 | 70 |
The hypothetical VPP has 10 power suppliers who purchase power from the main grid and sell it to the VPP. A one day (24h) simulation was performed with a 1 hour time step. The 24h load curve in VPP during one day is shown in FIG. 2. The maximum increase and decrease range of the hourly load parameter is respectively 8% and 7%. The load increase and decrease elastic coefficients were set to 0.04 and 0.03, respectively. To model the load transfer based demand response, the peak load period is taken to be the period when the sum of all bus loads is greater than 3.25 MW.
Data modeling behavior of an electric vehicle. The system shown in fig. 1 includes 2059 electric vehicles distributed over 32 VPP buses. Suppose that a connection bus of an electric vehicle to a power grid is specified. The number of electric vehicles per bus is shown in fig. 3. Fig. 4 shows the number of the grid-connected electric vehicles and the number of the traveling vehicles per hour in a day.
(2) Simulation verification
The simulation assumes two scenarios:
scene 1: electric automobile disordered charging without participating in demand response
Scene 2: and the intelligent charging of the electric automobile is based on price demand response.
In the simulation, the charge and discharge costs of the electric vehicle were set to 70$/MW and 90$/MW, respectively. The cost of charging energy due to the reduction in travel distance is considered to be 50 $/MW. Electric vehicles may benefit from participating in demand response programs. Assuming a load interruption cost of 200$/MW, the maximum reduction in electric vehicle travel distance is 30%.
the scenario assumes that all electric vehicles do not participate in the demand response scheme, and lack discharge capability at the same time. In addition, the load of all system buses remains fixed, provided that other loads also avoid participating in price-based demand response schemes. According to the simulation result, the value 6721.5 $ofthe objective function of scenario 1 is obtained.
For the uncontrolled charging situation of the electric vehicle, the power required for charging is significantly increased in some time periods due to the uncoordinated distribution of the electric vehicle, which may cause the VPP line to be congested and overloaded. As can be seen from fig. 5, the power consumption at the VPP main peak load increases and the load peak becomes more severe. Therefore, the need for intelligent charging of electric vehicles is undoubted.
B. And (3) a scene two simulation result:
under the scene, the electric automobile participates in price-based demand response, orderly and intelligently charges, and can transfer the load from peak time to off-peak time. The cost function value obtained in this scenario is 6387.36 $. Using price-based demand response reduces energy costs compared to scenario 1. The related energy cost is reduced by 0.62 percent.
Fig. 6 shows the use of price-based demand response to shift load from peak hours to off-peak hours, such that load decreases during peak hours and increases during off-peak hours. During peak loads, the grid-connected electric vehicle is discharged, and therefore the operation of the more costly unit may be delayed. The peak electricity utilization period is determined by the load of the power grid, and is 10: 00 to 9: 00. between 18 and 22 hours, the electric vehicle will supply peak loads through discharge. However, once the grid load drops during the night, the vehicle begins to charge to fill the hole in the load curve. In this way, it is no longer necessary to shut down the baseload generator, thereby eliminating the expense associated with re-operation. The need to purchase energy from expensive energy sources is reduced, as is the operating cost of the VPP. Fig. 7 is a comparison of power over a 24h time interval before and after the load response of scene 1 and scene 2.
From the simulation result, the demand response scheme based on price and the intelligent charging of the electric automobile reduce the load peak value, and the load curve tends to be smooth. This will further reduce the VPP's line pressure, power consumption and operating costs. Demand response can be considered a powerful tool for achieving more optimal operation of a VPP, providing greater economic and technical advantages.
The simulation results verify the effectiveness and the practicability of the model constructed by the method. The model can provide a decision maker with a larger choice space, so that an investor can make optimal planning decisions under more conditions, and the running cost of the established VPP is increased.
The above is only a preferred embodiment of the present invention, and it should be noted that: it will be apparent to those skilled in the art that some of the parameters may be adjusted without departing from the principles of the invention, and such adjustments should be considered within the scope of the invention.
The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are merely preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not for limiting the scope of the present invention, and on the contrary, any improvement or modification made based on the spirit of the present invention should fall within the scope of the present invention.
Claims (12)
1. A virtual power plant operation optimization method considering demand response and electric automobiles is characterized by comprising the following steps:
step 1, constructing a demand response strategy of a virtual power plant based on time-of-use electricity prices and interruptible loads, and transferring partial loads from a peak to a valley;
step 2, on the basis of the step 1, a mixed integer linear programming model of the virtual power plant is constructed by combining the generated energy of the DG, the charging and discharging of the electric automobile and the demand response;
and 3, solving the mixed integer linear programming model of the virtual power plant obtained in the step 2 to obtain the operating conditions of the virtual power plant, issuing the solving result to the controller, and executing the solving result on the day of the operation of the virtual power plant.
2. The method of claim 1 for optimizing the operation of a virtual power plant by considering demand response and electric vehicles, wherein the method comprises the following steps:
the step 1 specifically comprises the following steps:
step 1.1, considering demand response based on time-of-use electricity price and demand response of a virtual power plant constructed based on excitation interruption load, transferring partial load from a peak to a valley, and realizing load balancing;
step 1.2, constructing a demand response constraint condition of a virtual power plant, wherein the total power consumption of a load is kept unchanged during optimization;
and 1.3, constructing a load interruption cost function.
3. The method of claim 2 for optimizing the operation of a virtual power plant by considering demand response and electric vehicles, wherein the method comprises the following steps:
in step 1.1, load balancing is expressed as the following equation (1),
D(L，t)＝d(L，t)+dup(L，t)-ddo(L，t) (1)
in the formula:
d (L, t) represents the power consumption of the load L at the time t after the time-of-use electricity price is executed,
d (L, t) represents the predicted power consumption of the load L at time t,
dup(L, t) represents the power consumption of the load L increased in response to the original price change at time t,
ddo(L, t) represents the power consumption of the load L reduced in response to the change in the original price at time t.
4. The method of claim 2 for optimizing the operation of a virtual power plant by considering demand response and electric vehicles, wherein the method comprises the following steps:
in step 1.2, the total power consumption of the load should remain unchanged, expressed by the following equation (2),
in the formula:
t denotes the optimization duration.
5. The method of claim 4 for optimizing the operation of a virtual power plant by considering demand response and electric vehicles, wherein the method comprises the following steps:
in step 1.3, the load break cost function is expressed as the following equation (7),
CIL(j，t)＝β(j)QIL(j，t) (7)
in the formula:
CIL(j, t) represents the cost of outage reduction,
beta (j) represents the cost of an interrupt or the cost of a delay load j,
QIL(j, t) represents the amount of power that the interruptible load decreases at time t.
6. The method of claim 5, wherein the method comprises the following steps:
the step 2 specifically comprises the following steps:
step 2.1, constructing a virtual power plant single-day scheduling cost function, comprising the following steps: the cost part of DG power generation of the virtual power plant, the cost part of receiving power from a main power grid, the cost part of electric vehicle charging and discharging, the cost part of load interruption and the cost part of electric vehicle response;
and 2.2, constructing a constraint condition of a virtual power plant single-day scheduling cost objective function, wherein the virtual power plant single-day scheduling cost objective function is limited and constrained by an actually existing power grid and demand response.
7. The method of claim 6 for optimizing the operation of a virtual power plant by considering demand response and electric vehicles, wherein the method comprises the following steps:
in step 2.1, the virtual power plant single-day scheduling cost function is expressed by the following formula (8),
f＝f1+f2+f3+f4+f5 (8)
in the formula:
f represents a virtual power plant single-day scheduling cost function,
f1represents the cost of the electricity generated by the DG,
f2representing the cost of receiving power from the main grid,
f3represents the cost of charging and discharging the electric vehicle,
f4which represents the cost of interrupting the load and,
f5representing the cost of electric vehicle demand response.
8. The method of claim 7 for optimizing the operation of a virtual power plant by considering demand response and electric vehicles, wherein the method comprises the following steps:
step 2.1 specifically comprises:
step 2.1.1, constructing a cost function of DG power generation of the virtual power plant, which is expressed by the following formula (9),
in the formula:
f1which represents the cost of the electricity generation,
PDG(DG, t) represents the power generation of the DG unit at time t,
CDG(DG, t) represents the cost of electricity generation of the DG unit at time t,
NDGrepresenting the number of available DG unit resources;
step 2.1.2, a cost function for receiving power from the main grid is constructed, expressed by the following equation (10),
in the formula:
f2representing the cost of receiving power from the main grid,
PG(t) represents the amount of power received from the main grid at time t,
CG(t) represents the amount of electricity purchased from the grid at time t,
when VPP injects power into the main grid at time t, PGThe amount of (t) is negative, when f2Is a negative value;
step 2.1.3, constructing a cost function of charging and discharging of the electric vehicle, which is expressed by the following formula (11),
in the formula:
PDisCharge(V, t) represents the discharge power of the vehicle V at time t,
CDisCharge(V, t) represents the discharge price of the vehicle V at time t,
PCharge(V, t) represents the charging power of the vehicle V at time t,
CCharge(V, t) represents the charge price of the vehicle V at time t,
NVrepresenting the total number of electric vehicles;
step 2.1.4, a cost function of the interrupt load is constructed, expressed by the following equation (12),
in the formula:
f4the cost of the interrupt load is represented,
PIL(L, t) represents the amount of power used by the load L at the time t,
CIL(L, t) represents the price of the power source at time t,
NILrepresenting the number of interruptible loads;
step 2.1.5, constructing a cost function of the response of the electric automobile, which is expressed by the following formula (13),
in the formula:
ETripRed(V, t) represents the electric power reduced by the reduction of the running distance of the vehicle V at the time t,
CTripRed(V, t) represents a vehicle VE of (A)TripRed(V, t) price at time t,
z (V) represents a travel time change of the vehicle V, is a binary variable,
CShift(V) represents the price of the travel time change of the vehicle V.
9. The method of claim 8, wherein the method comprises the following steps:
the step 2.2 specifically comprises:
step 2.2.1, constraints of the generated energy and the power consumption are constructed, which are expressed by the following formula (15),
step 2.2.2, constructing the limit of the generation amount of each DG of the virtual power plant, which is expressed by the following formula (16),
in the formula:
PDGmin(DG, t) represents the minimum value of the DG power generation amount at time t,
PDGmax(DG, t) represents the maximum value of DG power generation at time t;
step 2.2.3, constructing a virtual power plant to accept the electric energy limit of the main power grid, which is expressed by the following formula (17),
in the formula:
PGmin(t) represents the minimum value of electrical energy received from the main grid at time t,
PGmax(t) represents the maximum value of the power received from the main grid at time t;
step 2.2.4, constructing an electric vehicle charging mode constraint, and defining two binary decision variables X (V, t) and Y (V, t); x (V, t) is related to the charge mode, Y (V, t) is related to the discharge mode, and is expressed by the following equation (18),
in the formula:
x (V, t) represents a charge mode decision variable, 1 only when the vehicle is in charge mode,
y (V, t) represents a discharge mode decision variable, which is 1 only when the vehicle is in discharge mode;
step 2.2.5, constructing an electric vehicle battery energy storage constraint, represented by the following formula (19),
in the formula:
EStore(V, t) represents the stored energy of the vehicle V battery at the current time interval,
EStore(V, t-1) represents the charge level of the battery at a time interval preceding the vehicle V,
PCharge(V, t) represents the amount of charge of the previous time interval,
ETrip(V, t) represents the current interval trip electric energy consumption,
PDisCharge(V, t) represents the amount of discharge of the battery at the current interval;
step 2.2.6, constructing a battery charging amount limit of the electric automobile, which is expressed by the following formula (20),
in the formula:
EBatCap(V) represents the battery capacity of the vehicle V,
ηc(V) represents the charging efficiency of the vehicle V;
step 2.2.7, constructing the discharge capacity limit of the battery of the electric automobile, which is expressed by the following formula (21),
in the formula:
ηd(V) represents the discharge efficiency of the vehicle V;
step 2.2.8, constructing the energy storage limit of the battery of the electric automobile, which is expressed by the following formula (22),
at step 2.2.9, constructing an electric vehicle trip electric energy demand limit, represented by the following equation (23),
in the formula:
EMinCharge(V, tLast) represents the minimum storage amount of battery power before last trip.
10. The method of claim 9 for optimizing the operation of a virtual power plant by considering demand response and electric vehicles, wherein:
in step 3, before issuing the solution result to the controller for execution, the method further includes: the feasibility and the correctness of the virtual power plant operation optimization method considering the demand response and the electric automobile are verified through simulation.
11. The method of claim 10 for optimizing the operation of a virtual power plant by considering demand response and electric vehicles, wherein:
the simulation verification comprises the steps of carrying out simulation on an electric automobile unordered charging scene which does not participate in demand response and an electric automobile intelligent charging scene based on price demand response, and comparing simulation results.
12. The utility model provides a consider demand response and electric automobile's virtual power plant operation optimization system which characterized in that:
operating a virtual plant operation optimization method taking into account demand response and electric vehicles according to any one of claims 1-11.
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